While typical non-invasive methods for measuring brain functions include fMRI, PET, MEG (magnetoencephalography) at present, the fMRI is considered to have the highest spatial resolution of data among them and is widely used.
The fMRI is a method for imaging various kinds of physical quantity allowing to locate activated brain regions as a measuring quantity, and is an effective technique for measuring brain functions (refer to Non-patent Documents 1 and 2). While reflecting the proton density, longitudinal relaxation time T1, and transverse relaxation time T2 of tissues in a living body as same as the principle of an anatomical MRI which images a brain structure, fMRI particularly has a feature to detect an increase in blood flow volume in an activated brain region. It is known that the blood flow volume will locally increase in the activated brain region and hemoglobin in blood differs in its magnetic properties between a state that oxygen is bound thereto (oxygenated hemoglobin) and a state that it is released therefrom (deoxygenated hemoglobin). It is considered that fMRI signals (BOLD signals) in an active region will increase since the amount of deoxygenated hemoglobin which disturbs a magnetic field is decreased in the increased arterial blood. Hence, the use of fMRI can locate regions in the brain related to the task (active region), by following a change in BOLD signal when a subject is performing a certain task.
Typical techniques to analyze the time series data of BOLD signals measured by fMRI includes SPM (Statistical Parametric Mapping) based on a general linear model (refer to Non-patent Document 1), a data analysis based on a principal component analysis and an independent component analysis (refer to Non-patent Document 3). These techniques have features in which results in which the time series data of the BOLD signals is individually subjected to statistical processing for every three-dimensional pixel (Voxel) is output as an image to locate activated brain regions.
In the above analysis techniques, however, there exists an issue that the network structures of neurons are not taken into consideration upon analyzing data. Many neurons constitute a complicated network via synapses in a brain, and it is considered that the brain realizes a higher brain function as a whole by mutual cooperation of each region thereof via such a neural network according to recent knowledge of brain science. For example, when the subject performs a certain task, it is observed a phenomenon that a plurality of regions are activated in the brain. Then, the analysis of this phenomenon using the above analysis techniques enables to locate each active region, but it is difficult to locate a connection between the active regions.
This has an underlying cause as follows. Since BOLD signals measured by fMRI are based on blood flow volume as described above, the activity of a gray matter (cell body of the neuron) having a relatively high blood flow volume in the brain is captured, but the activity of a white matter (axon of the neuron, or nerve fiber) having relatively low blood flow volume is hardly captured by fMRI.
Meanwhile, as a method for capturing a running direction of a nerve fiber group, which is the basis of the neural network structures, it has recently drawn attention a DTI (Diffusion Tensor Imaging) method in which the degree of proton diffusion in a body tissue is measured as a new observation quantity of MRI (refer to Non-patent Document 4). When a normal anatomical MRI is utilized, a nerve fiber becomes a high signal in a T1 weighted image and a low signal in T2 weighted image. The reason that the nerve fiber is converted into the high signal in the T1 weighted image is in presence of myelin. The myelin is composed of a lipid with a double-layer structure and a huge protein and takes a form to arrange along the running direction of the nerve fiber. Hence, there occurs anisotropy that the diffusion constant of protons is large in the running direction of the nerve fiber and is small in a direction perpendicular to it. DTI is the technique of measuring the anisotropy of diffusion by applying MPG (Motion Probing Gradient): {right arrow over (G)}=(Gx,Gy,Gz)T in order to emphasize the diffusion of protons. It is the technique, for example, to measure the intensity S′(l, m, k, i) of a BOLD signal obtained by ST (Stejskal-Tanner) pulse sequence in which STG (Stejskal-Tanner Gradient) pulses for diffusion detection are added to before and after SE (Spin Echo) pulses. Here “l”, “m” and “k” are positive discrete variables representing the position of a voxel and represent an X coordinate, Y coordinate and Z coordinate of the voxel, respectively. In addition, “i” is a positive discrete variable representing a measuring time. The intensity S′(l, m, k, i) of the BOLD signal can be written asS′(l,m,k,i)=ρ′(l,m,k,i)exp(−b{right arrow over (G)}T(l,m,k){right arrow over (G)}).  (1)
When a diffusion weighted image is generated, the diffusion tensor:
                              D          ⁡                      (                          l              ,              m              ,              k                        )                          =                  (                                                                                          D                    ll                                    ⁡                                      (                                          l                      ,                      m                      ,                      k                                        )                                                                                                                    D                    lm                                    ⁡                                      (                                          l                      ,                      m                      ,                      k                                        )                                                                                                                    D                    lk                                    ⁡                                      (                                          l                      ,                      m                      ,                      k                                        )                                                                                                                                            D                    ml                                    ⁡                                      (                                          l                      ,                      m                      ,                      k                                        )                                                                                                                    D                    mm                                    ⁡                                      (                                          l                      ,                      m                      ,                      k                                        )                                                                                                                    D                    mk                                    ⁡                                      (                                          l                      ,                      m                      ,                      k                                        )                                                                                                                                            D                    kl                                    ⁡                                      (                                          l                      ,                      m                      ,                      k                                        )                                                                                                                    D                    km                                    ⁡                                      (                                          l                      ,                      m                      ,                      k                                        )                                                                                                                    D                    kk                                    ⁡                                      (                                          l                      ,                      m                      ,                      k                                        )                                                                                )                                    (        2        )            in Equation (1) will be an object of the data analysis. Here ρ′(l, m, k, i) represents the intensity of a BOLD signal with no application of MPG, which is an object of data analysis in the normal brain function analysis, and “b” is a parameter representing the strength of MPG. Note that ρ′(l, m, k, i) is represented by
                                          ρ            ′                    ⁡                      (                          l              ,              m              ,              k              ,              i                        )                          ∝                              f            ⁡                          (              v              )                                ·                                    ξ              ′                        ⁡                          (                              l                ,                m                ,                k                ,                i                            )                                ·                      (                          1              -                              exp                ⁡                                  (                                      -                                                                  T                        R                                                                    T                        1                                                                              )                                                      )                    ·                      exp            ⁡                          (                              -                                                                  ⁢                                                      T                    E                                                        T                    2                                                              )                                                          (        3        )            where f(v) represents a flow velocity, TR a repetition time, TE an echo time; and ξ′(l, m, k, i) a proton density.
There has been currently used a brain function analysis method in which Regions of Interest (ROI) in a brain are connected by a diffusion tensor data D(l, m, k) after analyzing the time series data of BOLD signals ρ′(l, m, k, i) obtained by fMRI measurement (refer to Non-patent Document 5).
Non-patent Document 1: “Human Brain Function: 2nd-Ed.”, Richard S. J. Frackowiak, et al, ELSEVIER ACADEMIC PRESS, 2004
Non-Patent Document 2: “Image of Mind”, M. I. Posner and M. E. Raichle, W H Freeman & Co, 1997
Non-patent Document 3: “Independent Component Analysis: Theory and Applications”, T. W. Lee, Kluwer Acadmic, 1988
Non-patent Document 4: “Korede wakaru diffusion MRI” S. Aoki, O. Abe, Syuujyun sha, 2002
Non-patent Document 5: “Combined functional MRI and tractography to demonstrate the connectivity of the human primary motor cortex in vivo”, Guye M, et al., Neuroimage, Vol. 19, pp. 1349-1360, 2003